Decentralized data casting in an interest aware peer network

ABSTRACT

Systems and methods for data casting in an interest aware peer network are provided. The method comprises determining whether a set of interests associated with a node in a network is updated, wherein the set of interests comprises one or more topics IDs, wherein each topic ID corresponds to a topic of interest associated with said node; distributing the set of interests associated with the node to one or more neighboring nodes by way of a first distribution scheme until N nodes in the network are aware of the updated set of interests for the node; providing each node in the network with a mechanism to determine value of a topic of interest in a set of interests received in association with a node based on a topic ID included in the set of interests for the node.

COPYRIGHT & TRADEMARK NOTICES

A portion of the disclosure of this patent document may containmaterial, which is subject to copyright protection. The owner has noobjection to the facsimile reproduction by any one of the patentdocument or the patent disclosure, as it appears in the Patent andTrademark Office patent file or records, but otherwise reserves allcopyrights whatsoever.

Certain marks referenced herein may be common law or registeredtrademarks of the applicant, the assignee or third parties affiliated orunaffiliated with the applicant or the assignee. Use of these marks isfor providing an enabling disclosure by way of example and shall not beconstrued to exclusively limit the scope of the disclosed subject matterto material associated with such marks.

TECHNICAL FIELD

The disclosed subject matter relates generally to optimizing datacasting in an interest aware peer-to-peer network, and more particularlyto a hash-based distribution algorithm for disseminating information insuch network.

BACKGROUND

In an interest aware communication network, the participating networknodes generally broadcast their interest in a topic by way ofpublication of their topics of interest to other nodes. In return, othernodes that are interested in a published topic may subscribe to thattopic or provide messages related to that topic to a node with acorresponding interest. This communication paradigm is often referred toas publish-subscribe (pub-sub) broadcast or multicast (e.g., data cast)network.

A publisher node is a node that publishes a message about a topic onlogical channels associated with identified topics. A subscriber node isa node that is interested in a message published on a certain topic andthus subscribes to the topic and receives messages published on thattopic. Publishers and subscribers are not directly linked or associatedwith each other in this paradigm because they don't need to know thenetwork address of one another. Instead, a topic identifier isassociated with each topic and that association is preserved in acentralized system that mediates between the publishers and subscribers.

Such centralized systems rely on the discovery and distribution oftopic-group memberships for each node in the network. A topic-groupmembership refers to a group of nodes that subscribe to a certain topic.Some systems may have thousands of nodes, thousands of topics andthousands of messages generated on those topics by said nodes. In suchlarge systems, a great multitude of different topics are used forpub-sub communication, system monitoring and control, as well as otherprocesses.

Each pub-sub process may be interested in a different subset of theavailable topics. Because publishing or subscribing to a topic indicates“interest” in that topic, a service that delivers the identity ofsubscribers (or publishers, or both) to a topic is called aninterest-aware membership service. The interest aware membership serviceprovides to the application using it the interest of nodes in thesystem. The interest, when presented to the application, may berepresented as a set of topic names.

In some implementations, a membership service may represent the interestof a node as a list of string identifiers. This can be highlyinefficient, however, where topic names are generally expressed asstrings that can be quite lengthy. It is not uncommon, for example, thata topic name is a few hundred characters long. Especially in adecentralized data-center the large number of nodes and topics, combinedwith long topic names consume excessive communication bandwidth andmemory. That is, a substantial amount of overhead is generated each timethe topic names for thousands of nodes are interchanged, andparticularly when a new topic is generated or when the interest of anode in a topic changes.

To remedy the above, a central directory service (i.e., registry) may beutilized to track the topic names and provide an index for each topicname where the index is used to associate the numerous topic names withthousands of nodes with an interest in said topic. Interest awaredissemination algorithms may be implemented using topic indices, andinverse lookup tables into the registry in order to link a topic indexto a topic name A central registry is generally risky in case of adisaster (i.e., data recovery from a single point of failure is highlyundesirable) and also poses the possibility of a performance bottleneck.Moreover, a centralized registry typically needs ongoing configurationand maintenance, thus generating additional administrative overhead.

SUMMARY

For purposes of summarizing, certain aspects, advantages, and novelfeatures have been described herein. It is to be understood that not allsuch advantages may be achieved in accordance with any one particularembodiment. Thus, the disclosed subject matter may be embodied orcarried out in a manner that achieves or optimizes one advantage orgroup of advantages without achieving all advantages as may be taught orsuggested herein.

In accordance with one embodiment, a data casting method in an interestaware peer network comprising a plurality of nodes is provided. Themethod comprises determining whether a set of interests associated witha node in the network is updated, wherein the set of interests comprisesone or more topics IDs, wherein each topic ID corresponds to a topic ofinterest associated with said node; distributing the set of interestsassociated with the node to one or more neighboring nodes by way of afirst distribution scheme until N nodes in the network are aware of theupdated set of interests for the node; providing each node in thenetwork with a mechanism to determine value of a topic of interest in aset of interests received in association with a node based on a topic IDincluded in the set of interests for the node; and wherein a node in thenetwork subscribes or publishes to a topic of interest associated withsaid node based on the topics of interest associated with the node.

The first distribution scheme may be implemented based on a gossipalgorithm. In one embodiment, a hash function (e.g., a distributed hashtable) is used to derive the topic ID from the topic of interest. Acache or other type of storage medium including a global set of topicsof interest may be updated each time a new topic of interest isgenerated by a node in the network, and wherein the global set of topicsof interest is distributed to the N nodes in the network by way of asecond distribution algorithm. The distribution algorithms may beimplemented based on a gossip algorithm.

Depending on implementation, the gossip algorithm comprises a method ofdistribution among neighboring nodes in a network, wherein each nodedistributes certain information to its neighbor nodes until the N nodesin the network have received said certain information. In one example, Xnodes in the network may distribute certain information to a randomselection of Y nodes in the network, and said Y nodes which are randomlyselected repeat the same distribution method until the N nodes in thenetwork have received said certain information.

In another example, each node in the network distributes certaininformation to its neighbor nodes until the N nodes in the network havereceived said certain information. Optionally, a gossip algorithm maydistribute information among X nodes in a network, wherein each of the Xnodes distributes certain information to a random selection of Y nodesin the network, and said Y nodes which are randomly selected repeat thesame distribution method until the N nodes in the network have receivedsaid certain information.

In accordance with one or more embodiments, a system comprising one ormore logic units is provided. The one or more logic units are configuredto perform the functions and operations associated with theabove-disclosed methods. In yet another embodiment, a computer programproduct comprising a computer readable storage medium having a computerreadable program is provided. The computer readable program whenexecuted on a computer causes the computer to perform the functions andoperations associated with the above-disclosed methods.

One or more of the above-disclosed embodiments in addition to certainalternatives are provided in further detail below with reference to theattached figures. The disclosed subject matter is not, however, limitedto any particular embodiment disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed embodiments may be better understood by referring to thefigures in the attached drawings, as provided below.

FIG. 1 illustrates an exemplary computing network environment inaccordance with one or more embodiments implemented to support aninterest aware peer-to-peer data cast mechanism.

FIGS. 2A and 2B are exemplary block flow diagrams of methods ofdistributing and tracking interests in data topics, in accordance withone embodiment.

FIGS. 3A and 3B illustrate exemplary models for utilizing a hash-baseddistribution algorithm to data cast in an interest aware peer network,in accordance with one embodiment.

FIGS. 4A and 4B are block diagrams of hardware and software environmentsin which the disclosed systems and methods may operate, in accordancewith one or more embodiments.

Features, elements, and aspects that are referenced by the same numeralsin different figures represent the same, equivalent, or similarfeatures, elements, or aspects, in accordance with one or moreembodiments.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following, numerous specific details are set forth to provide athorough description of various embodiments. Certain embodiments may bepracticed without these specific details or with some variations indetail. In some instances, certain features are described in less detailso as not to obscure other aspects. The level of detail associated witheach of the elements or features should not be construed to qualify thenovelty or importance of one feature over the others.

Referring to FIG. 1, a distributed system with N nodes (i.e., processes)is illustrated, in accordance with one embodiment. Each node islogically connected to a set of other nodes (i.e., neighbor nodes) in,for example, an overlay network. In one exemplary implementation, aconnectivity graph imposed by the overlay network may be partially oroptionally fully connected such that a path composed of one or moreoverlay connections is established from every node to every other node.The overlay network may be an unstructured, structured, or a hybridoverly network.

A topic of interest (TOI) may be expressed as character strings. A nodemay become a subscriber, or a publisher, or both, on any given topic. Anode is said to be interested in a topic if it is a subscriber orpublisher to that topic. Such pub-sub environment may be utilized toform an interest-aware network environment in one or more embodiments.An interest-aware implementation maintains the identity of each nodetogether with the set of topics that node is interested in.

The interest may be defined as the set of topics to which a nodesubscribes. For example, a topic of interest may be represented by acharacter string that identifies a ticker for a company on the stockmarket. As shown in FIG. 1, for example, node N1 has subscribed totopics of interest defined by character strings NYSE/C, NYSE/IBM,NASDAQ/TEVA, etc. Depending on implementation, a node may drop or add(i.e., unsubscribe or subscribe, respectively) a TOI from the set oftopics associated with the node.

Using the above implementation, the nodes are able to share or data castinformation with the other nodes in the network. The information may beshared in a peer-to-peer type overlay network using TCP/IP protocol, forexample. This overlay system may be optionally used to implementbroadcast and multicast on the overlay network, instead of usingIP-multicast protocol, for example. A node may designate one or moreTOIs and notify its peers of the TOIs, as provided in further detailbelow.

The one or more TOIs for a node may be included in a set (e.g., set ofinterest or (SOI)). The SOI may be disseminated to other nodes in thenetwork so that the other nodes may learn about that nodes interest inone or more TOIs. In one example embodiment, a distribution algorithm isutilized to distribute the topic information (e.g., the TOIs or the SOI)for a node among its peer nodes. Based on the distribution algorithm,each peer node then redistributes the topic information to its peers andso on, until desirably all the nodes in the network are aware of theTOI's of the target node.

Since a TOI is generally represented by a string of characters (e.g.,text), distribution of the topic information as strings of charactersmay not be efficient depending on the size of the network. That is, asubstantially large volume of bytes associated with the characterstrings will be communicated if the TOIs or SOIs of all the nodes in alarge network are to be distributed using the distribution algorithm.Thus, in one embodiment, instead of implementing the SOI based oncharacter string TOIs, a compressed version of the TOIs is utilized.

In other words, each TOI is compressed into a topic id (TID), forexample, and the TIDs are grouped to implement an SOI for acorresponding node. In this manner, an SOI for a node may be representedby a substantially smaller number of bytes and help reduce theassociated overhead with disseminating the topic information for a nodeby way of the distribution algorithm. As provided in more detail below,a TID may be generated by way of a one-way hash function or a reversiblecompression scheme depending on implementation.

Referring to FIG. 2A, when a node generates a TID for a new TOI, thenode desirably implements an SOI or an updated SOI for that node usingthe new TID (S210). That is, for example, if the SOI associated with thenode includes TID1, TID2 and TID3, associated with TOI1, TOI2 and TOI3respectively, then a new TID4 generated for a new TOI4 for that node isadded to the SOI for the node. Thus, the updated SOI includes TID1,TID2, TID3, and TID4. Once it is determined that the SOI is updated, theupdated SOI is then distributed to the node's peers and neighbors by wayof a distribution algorithm, for example (S220, S230).

Referring to FIG. 2B, in one implementation, a node stores in a cachearea (e.g., a localized storage medium) a copy of TOIs for all nodes(S240). As such, when a new TOI is generated by a node, the nodedistributes the new TOI to the other nodes in the network. Once the newTOI arrives at the peer nodes (S250), the receiving node adds the newTOI to its cache where the TOIs for other nodes are stored (S260).Different approaches and schemes may be utilized to accomplish the abovedissemination scheme.

For example, in one embodiment, the list of one or more TOIs (oralternatively the list of all TOIs) for a node may be disseminated viathe distribution algorithm to other nodes, so that the other nodes knowthat a new TOI has been generated. Optionally, each node includes aglobal (i.e., network-wide) list of all TOIs. Since, a new TOI is notgenerated as often as messages that relate to a TOI, distributing aglobal list of TOIs among the nodes will not create substantialoverhead. In some implementations, instead of distributing the globallist, the newly generated TOI is distributed to the peer nodes using adistribution algorithm.

Once the other nodes know about the new TOI, they will be able to derivethe TID for that TOI by applying a predetermined hash algorithm to theTOI. That is, in one implementation, all nodes use the same hashalgorithm so they will arrive at the same TID, if they run it on thesame TOI. Thus, each node in its cache or other storage area mayimplement a lookup table that allows the node to match a newly receivedTID with a TOI in the global list of TOIs.

In one embodiment, instead of using a hash value, a reversiblecompression algorithm may be utilized to determine the TOI for a newlyreceived TID. In this manner, there may be no need for generating anddistributing a global list of TOIs every time a new TOI or TID isgenerated by a node. As such, once a node receives an unknown TID thatis generated from applying a compression algorithm to a TOI, the nodemay apply the reverse compression algorithm to the TID to determine theTOI associated with that TID.

A distributed hash algorithm may be utilized to match a TID to a TOIusing shortened hash values. In such an implementation, a distributedhash table (DHT) is used to provide a lookup service using (key, value)pairs stored in the DHT. As provided in further detail below, using theDHT values, a node can efficiently retrieve the value (e.g., TOI)associated with a given key (e.g., TID). Responsibility for maintainingthe mapping from keys to values is distributed among the nodes in such away that a change in the set of participants causes a minimal amount ofdisruption. This allows DHTs to scale to extremely large numbers ofnodes and to handle continual node arrivals, departures, and failures.

A distributed hash function helps further reduce the overhead on thepub-sub network by generating even smaller TIDs. It is noteworthy thatthe generated shortened hash values have a higher chance of collision(i.e., the same shortened TID may represent more than one TOI). As notedbriefly above and as it will be discussed in further detail below, thetask of resolving the collision may be distributed among the nodes inthe network, such that if there are N nodes in the network and a totalof M TOIs are assigned, each node is assigned approximately M/N TOIs tohandle, for collision purposes.

Accordingly, one or more embodiments may be implemented based on thefollowing schemes: a compact representation for topic names using a hashfunction; a distribution based interest-aware membership algorithm thatuses a compact topic representation; a distributed algorithm thatmaintains for each node a list of all topic names with their hashvalues. That is, topic names may be encoded with either a fixed orvariable-length identifier generate by a hash function.

In one embodiment, a secure hash function which results in fixed lengthidentifiers (e.g., 160 or 128 bit long) may be used. Because of thecryptographic nature of such hash functions, generation of the same hashvalue for different topic names will be extremely unlikely. In case ashorter representation is needed, a variable size unique identifierusing a DHT may be used. A node may be dedicated in the DHT for eachtopic, and topic identifier creation may be serialized through thatnode.

A global inverse map for topic names may be provided such that a nodeholds a map of known topic names, along with their identifiers.Periodically or upon changes to the self-interest, a node compares thecontent of its map to the content of its overlay neighbor's maps. Ifthere is a difference, the nodes may exchange maps and set a new map tothe union of their respective older maps. A node may continue to comparemaps with its neighbors until its map is identical to the maps of itsneighbors. This process is implemented so that the nodes eventuallylearn about the newly generated topics (i.e., TOIs) and the respectiveidentifiers (i.e., TIDs).

In one embodiment, an interest-aware membership distribution algorithmrepresents the interest of each node based on the SOI for that node anda rumor (i.e., message) in the distribution algorithm is composed of thenode ID and its interest. Rumors are spread either (1) to the neighborsof the node in an overlay network, or (2) to a random selection of peersout of the entire network, for example, depending on the type ofdistribution algorithm used. The end result is that the interest-awaremembership algorithm delivers to every node, the interest of every othernode in the system, expressed as a list of identifier values (e.g., TOIsor SOIs).

The distribution algorithm is optionally fully distributed and thusavoids the disadvantages of a central registry scheme (i.e., singlepoint of failure, performance bottlenecks, administration overhead, andnon-symmetric design). In some embodiments, two distribution layers areimplemented. One layer maintains a consistent topic cache 320 that holdsthe topic names and the corresponding identifiers. The second layerimplements interest-aware membership using topic identifiers. This makesthe proposed algorithm very robust.

In some embodiments, the interest-aware distribution algorithm operateswith topic identifiers and thus conserves bandwidth and memory. Forexample, consider a system with approximately 100 nodes (i.e., servers)with about 6000 topics, and topic names that are 200 bytes long, onaverage. Each server may subscribe to about 200 topics on average.Representing the interest of a server in the above example using textualTOI representation scheme takes 200 (topics)*200 (bytes)=40000 bytes.

In contrast, representing the interest of a server in a peer-to-peerinterest aware environment noted above, using hash values (e.g., MD5hash), would take 200 (topics)*(16 bytes)=3200 bytes translating into a12.5 time reduction in description length. The interest of a node is thebasic unit of information that is transferred in a round of theinterest-aware distribution algorithm. Thus, the above exemplarydistribution algorithms lead to significant reduction in bandwidthusage. In a DHT-based, variable-length topic-identifier creationalgorithm, there is a potential for even greater savings in memory andbandwidth. For example, it is possible to represent the topic identifierwith about 5 bytes, which may lead to as much as a 40-time reduction indescription length and bandwidth.

In addition to the above, some embodiments also provide for shortermessage lengths for data messages that are transmitted between thenodes. Multicast or pub-sub data messages that are transmitted onprotocols that do not necessitate a two-way handshake between thepublisher and subscriber may need to include a topic identifier in everydata message. In the following, a more detailed description is providedon how the topic names may be represented in a compact manner by way ofencoding the topic names with either a fixed- or variable-lengthidentifier generate by a hash function.

Referring to FIG. 3B and for the sake of example, we assume that thereis an operation that takes as input the topic name and produces thecompact representation (e.g., “Topic-ID←createTopic(String topicName).”Such process may be encapsulated in a topic ID generator module (e.g.,topic ID factory 310). In some embodiments, fixed length encoding isimplemented using a secure hash function, such as SHA1 or MD5, whichresults in fixed-length identifiers of 160 or 128 bit long,respectively.

Due to the cryptographic nature of these hash functions, generation ofthe same hash value for different topic names is extremely unlikely tothe extent that such functions are also called collision-free hashfunctions. In other embodiments, variable-length encoding is produced bygenerating a short fixed-length identifier, and using a DHT lookup inorder to extend this identifier with a variable number of bytes, in amanner that ensures uniqueness.

For example, referring to Table 1, L bits may be assigned to a shorthash value (SHV), and K bits may be assigned to encode the length (inbytes) of the extension bytes (EL). The variable length encoding lengthis then (K+L) bits plus (2^K−1) bytes. Normally (K+L) is chosen to be aninteger number of bytes.

TABLE 1 Example variable length encoding format Extension Short, fixedlength Extension bytes, from Length, in bytes Hash Value of zero up to2{circumflex over ( )}K − 1 (EL) topic name (SHV) bytes (EXT) K bits Lbits M bytes

A DHT implementation may route a <key,message> pair to a single targetnode, which is a function of the key. A target node that is in charge ofa key consistently replicates its state to its successor, so that incase of a failure, one of its successors takes over the role of being incharge of that key, in accordance with one or more embodiments. In onestructured overlay implementation nodes may be ordered in a ringaccording to a virtual ID derived from their name or physical addressusing an m-bit secure hash function (typically m is between 128 and 160bits).

When routing a <key,message> pair to a target node, a virtual ID may begenerated from the key using the same hash function it used to generatethe virtual IDs on the nodes. The node in the ring that will be incharge of handling the <key,message> pair (i.e., the target node) may bethe node that has, for example, the lowest virtual ID that is largerthan or equal to the virtual ID generated from the key (see FIG. 3A).The link choice in the above-discussed structured overlay and most otherstructured overlay DHTs allow for a message to be routed to its targetin O(Log(N)) hops, when N represents the number of nodes.

FIG. 3A illustrates an exemplary network with exemplary nodes N1, N8,N21, N32, N42, N51, N55 and provides an implementation for routing amessage with a key virtual ID=54, from Node N8. The target node for keyK54 is node N55, which has the lowest virtual ID that is larger than orequal to 54. Note that the target node for a key with virtual ID=56 willbe N1, in this example. Each node contains a cache of known topics andtheir identifiers, which is update using the distribution protocol. Thenode that creates a topic first checks whether it is in the cache. If itis not, then the following process takes place in the topic ID factory310 (see FIG. 3B).

In one embodiment, the node that creates a topic locally calculates theshort hash value (SHV), using a system-wide known function. The creatingnode then uses the SHV as the key for routing a message to a certaintarget node in the DHT. The message includes a directive to create atopic, the creator node identity, and the topic name, for example. Thetarget node in the DHT that receives the message stores, for each SHV, aset that includes all the unique topic names it ever received with thesame SHV, in the order in which they were first received (see Table 2).

The extension encodes the arrival order of the create topic directivefor topics with identical SHV, starting from zero. The extension encodesthe arrival order as an unsigned binary with the minimal number ofbytes. That is, zero is encoded in zero bytes (no extension), values1-255 are encoded in one byte, and values 256-65535 are encoded in twobytes and so on. The target DHT node returns to the creator node theextensions bytes. The extension is returned using a dedicated messagethat includes the length of the extension, the extension bytes, and thetopic name. The creator node inserts the topic name with the variablelength identifier into the topic cache 320. This new entry will bepropagated to all other nodes by a distribution service.

In some embodiments, the DHT target node may insert the topic name withthe variable length identifier into the topic cache 320. The creatornode may wait for the new topic name to be reflected in its own cache.If a target node receives a create request for a topic that alreadyexists, it returns the extension bytes.

TABLE 2 Example data structure in each DHT target node SHV A set oftopic names, by arrival order shv-1 Topic-A, Topic-B, . . . shv-2Topic-C . . . . . .

For example, let us choose K=4, L=28. A 28-bit hash code may begenerated from any character string by taking a 32-bit string hash-codeand dropping 4 bits from it (e.g., the upper four). As such, in mostcases, the variable length identifier will have a length of 32-bits. Inthe rare event that two or more topic names have the same 28-bit SHV, anextension may need to be added.

Depending on implementation, when two nodes try to create the same topicat the same time, the create requests will race towards the target node.The request received by the target node first will generate theextension bytes, the second will get the already created value, in oneor more example embodiments. The target node that is in charge of acertain key replicates its data-structure among S (configurable) of itssuccessors.

The replication may be consistent, using a mechanism liketwo-phase-commit, for example. Moreover, the target-node of a topic maybe agreed upon among the S+1 nodes that participate in storing the datastructures of that topic. This ensures that the data structures in theDHT target nodes remain consistent in the face of node failure. Thevariable-length identifier is, in an exemplary implementation, shorterthan the (secure hash) fixed-length identifier. However, its generationis more expensive and requires a DHT with consistently replicated nodes.

In some implementations, a global inverse map for topic names isprovided. Every node holds a map of known topic names, along with theiridentifiers. This map may be called the topic-cache (see FIG. 3B) Thetopic-cache supports lookup by topic-name or Topic-ID (TID), or insertof a pair {Topic-name, TID}. A distributed cache consistency protocolthat runs between the nodes allows the caches to remain in synch. Twoexamples of a consistency protocol are provided below.

In one or more embodiments, an anti-entropy scheme on the overlay isprovided. Periodically or upon the creation of a new topic, a nodecompares the content of its topic-cache with the content of its overlayneighbor's topic-cache. If there is a difference, the two nodes exchangemaps and set their new map to the union of their respective older maps.A node will continue to compare maps with its neighbors until its map issynchronized with the maps of its neighbors. This process allows thenodes to know the topics and the topic identifiers, as the nodescontinue to synchronize their global topic set with other nodes.

The synchronization process may be made more efficient if the mapcomparison is based on the exchange of cryptographic signatures ratherthan exchange of the complete map. When a node inserts a new pair{Topic-name,TID} into its cache, it may start a rumor to indicate thatsaid pair has been added and send it to a random selection of peers (notnecessarily neighbors). Peers that receive the rumor insert the pair totheir cache and distribute it further. This effectively amounts tobroadcasting the event to all the participants of the overlay. Thisprocess is complemented by a periodic but infrequent anti-entropy phase(e.g., pair-wise comparisons and swap of maps).

Both of the above example embodiments are variants of adistribution-style algorithm. The end result is that the topic-names andtheir respective Topic IDs are known to the participant nodes in theoverlay. In one embodiment, if a message arrives with a topic ID thatdoes not appear in the cache, the message is either retained until anupdate to the cache arrives or dropped. End-to-end reliabilitymechanisms may be used to track the delivery of lost messages.

In some embodiments, the interest-aware membership distributionalgorithm represents internally the self-interest 340 of each node usinga list of identifier values (TID) rather than a list of strings. A rumor(message) in the distribution algorithm is composed of the node ID andits interest (or changes in his interest), together with a logicaltime-stamp. Rumors are spread either (1) to the neighbors of the node inan overlay network, or (2) to a random selection of peers out of theentire network, depending on the variant of distribution used.

Desirably, when an interest aware membership service 330 delivers anevent to the application, it translates the interest representation fromTIDs to topic names using the topic cache 320 (see FIG. 3B). If thetranslation from TID to topic-name fails for a certain TID, which mayhappen when the translation is not yet in the cache, the interest-awaremembership may delay delivery of the event until the cache gets updatedwith the translation (e.g., from the topic-cache distribution protocol).Accordingly, the interest-aware membership algorithm delivers to a node,the interest of other nodes in the system, expressed as a list ofidentifier values, for example.

In different embodiments, the claimed subject matter may be implementedas a combination of both hardware and software elements, oralternatively either entirely in the form of hardware or entirely in theform of software. Further, computing systems and program softwaredisclosed herein may comprise a controlled computing environment thatmay be presented in terms of hardware components or logic code executedto perform methods and processes that achieve the results contemplatedherein. Said methods and processes, when performed by a general purposecomputing system or machine, convert the general purpose machine to aspecific purpose machine.

Referring to FIGS. 4A and 4B, a computing system environment inaccordance with an exemplary embodiment may be composed of a hardwareenvironment 1110 and a software environment 1120. The hardwareenvironment 1110 may comprise logic units, circuits or other machineryand equipments that provide an execution environment for the componentsof software environment 1120. In turn, the software environment 1120 mayprovide the execution instructions, including the underlying operationalsettings and configurations, for the various components of hardwareenvironment 1110.

Referring to FIG. 4A, the application software and logic code disclosedherein may be implemented in the form of computer readable code executedover one or more computing systems represented by the exemplary hardwareenvironment 1110. As illustrated, hardware environment 110 may comprisea processor 1101 coupled to one or more storage elements by way of asystem bus 1100. The storage elements, for example, may comprise localmemory 1102, storage media 1106, cache memory 1104 or othercomputer-usable or computer readable media. Within the context of thisdisclosure, a computer usable or computer readable storage medium mayinclude any recordable article that may be utilized to contain, store,communicate, propagate or transport program code.

A computer readable storage medium may be an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor medium, system,apparatus or device. The computer readable storage medium may also beimplemented in a propagation medium, without limitation, to the extentthat such implementation is deemed statutory subject matter. Examples ofa computer readable storage medium may include a semiconductor orsolid-state memory, magnetic tape, a removable computer diskette, arandom access memory (RAM), a read-only memory (ROM), a rigid magneticdisk, an optical disk, or a carrier wave, where appropriate. Currentexamples of optical disks include compact disk, read only memory(CD-ROM), compact disk read/write (CD-R/W), digital video disk (DVD),high definition video disk (HD-DVD) or Blue-ray™ disk.

In one embodiment, processor 1101 loads executable code from storagemedia 1106 to local memory 1102. Cache memory 1104 optimizes processingtime by providing temporary storage that helps reduce the number oftimes code is loaded for execution. One or more user interface devices1105 (e.g., keyboard, pointing device, etc.) and a display screen 1107may be coupled to the other elements in the hardware environment 1110either directly or through an intervening I/O controller 1103, forexample. A communication interface unit 1108, such as a network adapter,may be provided to enable the hardware environment 1110 to communicatewith local or remotely located computing systems, printers and storagedevices via intervening private or public networks (e.g., the Internet).Wired or wireless modems and Ethernet cards are a few of the exemplarytypes of network adapters.

It is noteworthy that hardware environment 1110, in certainimplementations, may not include some or all the above components, ormay comprise additional components to provide supplemental functionalityor utility. Depending on the contemplated use and configuration,hardware environment 1110 may be a desktop or a laptop computer, orother computing device optionally embodied in an embedded system such asa set-top box, a personal digital assistant (PDA), a personal mediaplayer, a mobile communication unit (e.g., a wireless phone), or othersimilar hardware platforms that have information processing or datastorage capabilities.

In some embodiments, communication interface 1108 acts as a datacommunication port to provide means of communication with one or morecomputing systems by sending and receiving digital, electrical,electromagnetic or optical signals that carry analog or digital datastreams representing various types of information, including programcode. The communication may be established by way of a local or a remotenetwork, or alternatively by way of transmission over the air or othermedium, including without limitation propagation over a carrier wave.

As provided here, the disclosed software elements that are executed onthe illustrated hardware elements are defined according to logical orfunctional relationships that are exemplary in nature. It should benoted, however, that the respective methods that are implemented by wayof said exemplary software elements may be also encoded in said hardwareelements by way of configured and programmed processors, applicationspecific integrated circuits (ASICs), field programmable gate arrays(FPGAs) and digital signal processors (DSPs), for example.

Referring to FIG. 4B, software environment 1120 may be generally dividedinto two classes comprising system software 1121 and applicationsoftware 1122 as executed on one or more hardware environments 1110. Inone embodiment, the methods and processes disclosed here may beimplemented as system software 1121, application software 1122, or acombination thereof. System software 1121 may comprise control programs,such as an operating system (OS) or an information management system,that instruct one or more processors 1101 (e.g., microcontrollers) inthe hardware environment 1110 on how to function and processinformation. Application software 1122 may comprise but is not limitedto program code, data structures, firmware, resident software, microcodeor any other form of information or routine that may be read, analyzedor executed by a processor 1101.

In other words, application software 1122 may be implemented as programcode embedded in a computer program product in form of a computer-usableor computer readable storage medium that provides program code for useby, or in connection with, a computer or any instruction executionsystem. Moreover, application software 1122 may comprise one or morecomputer programs that are executed on top of system software 1121 afterbeing loaded from storage media 1106 into local memory 1102. In aclient-server architecture, application software 1122 may compriseclient software and server software. For example, in one embodiment,client software may be executed on a client computing system that isdistinct and separable from a server computing system on which serversoftware is executed.

Software environment 1120 may also comprise browser software 1126 foraccessing data available over local or remote computing networks.Further, software environment 1120 may comprise a user interface 1124(e.g., a graphical user interface (GUI)) for receiving user commands anddata. It is worthy to repeat that the hardware and softwarearchitectures and environments described above are for purposes ofexample. As such, one or more embodiments may be implemented over anytype of system architecture, functional or logical platform orprocessing environment.

It should also be understood that the logic code, programs, modules,processes, methods and the order in which the respective processes ofeach method are performed are purely exemplary. Depending onimplementation, the processes or any underlying sub-processes andmethods may be performed in any order or concurrently, unless indicatedotherwise in the present disclosure. Further, unless stated otherwisewith specificity, the definition of logic code within the context ofthis disclosure is not related or limited to any particular programminglanguage, and may comprise one or more modules that may be executed onone or more processors in distributed, non-distributed, single ormultiprocessing environments.

As will be appreciated by one skilled in the art, a software embodimentmay include firmware, resident software, micro-code, etc. Certaincomponents including software or hardware or combining software andhardware aspects may generally be referred to herein as a “circuit,”“module” or “system.” Furthermore, the subject matter disclosed may beimplemented as a computer program product embodied in one or morecomputer readable storage medium(s) having computer readable programcode embodied thereon. Any combination of one or more computer readablestorage medium(s) may be utilized. The computer readable storage mediummay be a computer readable signal medium or a computer readable storagemedium. A computer readable storage medium may be, for example, but notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing.

In the context of this document, a computer readable storage medium maybe any tangible medium that can contain, or store a program for use byor in connection with an instruction execution system, apparatus, ordevice. A computer readable signal medium may include a propagated datasignal with computer readable program code embodied therein, forexample, in baseband or as part of a carrier wave. Such a propagatedsignal may take any of a variety of forms, including, but not limitedto, electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable storage medium may betransmitted using any appropriate medium, including but not limited towireless, wireline, optical fiber cable, RF, etc., or any suitablecombination of the foregoing. Computer program code for carrying out thedisclosed operations may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages.

The program code may execute entirely on the user's computer, partly onthe user's computer, as a stand-alone software package, partly on theuser's computer and partly on a remote computer or entirely on theremote computer or server. In the latter scenario, the remote computermay be connected to the user's computer through any type of network,including a local area network (LAN) or a wide area network (WAN), orthe connection may be made to an external computer (for example, throughthe Internet using an Internet Service Provider).

Certain embodiments are disclosed with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer programinstructions. These computer program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

These computer program instructions may also be stored in a computerreadable storage medium that can direct a computer, other programmabledata processing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablestorage medium produce an article of manufacture including instructionswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures.

For example, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts, orcombinations of special purpose hardware and computer instructions.

The claimed subject matter has been provided here with reference to oneor more features or embodiments. Those skilled in the art will recognizeand appreciate that, despite of the detailed nature of the exemplaryembodiments provided here, changes and modifications may be applied tosaid embodiments without limiting or departing from the generallyintended scope. These and various other adaptations and combinations ofthe embodiments provided here are within the scope of the disclosedsubject matter as defined by the claims and their full set ofequivalents.

What is claimed is:
 1. A data casting method in an interest aware peernetwork comprising a plurality of nodes, the method comprising:determining whether a set of interests associated with a node in thenetwork is updated, wherein the set of interests comprises one or moretopic IDs, wherein a topic ID corresponds to a topic of interestassociated with said node, wherein a first topic ID identifies a firstchannel of communication over which first content associated with thefirst topic ID is communicated to a first node, and wherein a first setof interests identifies one or more subscribers interested in receivingthe first content via the first node, and wherein an updated set ofinterests associated with the first node identifies an updated group ofsubscribers interested in receiving the first content; distributing theupdated set of interests associated with the first node to one or moreneighboring nodes by way of a first distribution scheme, until the totalnumber of the nodes of the network or a subset thereof in the networkare aware of the updated set of interests for the first node, whereinthe first distribution scheme is implemented based on a gossipalgorithm, wherein the gossip algorithm comprises a method ofdistribution of information among one or more first nodes in a network,wherein each of the one or more first nodes distributes information to arandom selection of one or more nodes among a plurality of neighboringnodes in the network, and said one or more neighboring nodes which arerandomly selected repeat the same distribution method at least onceuntil the total number of the nodes of the network or a subset thereofin the network have received said information; wherein the one or moreneighboring nodes determine value of a first topic of interest in thefirst set of interests received in association with the first node,based on the first topic ID included in the first set of interests forthe first node; and wherein the first node publishes or subscribes tocontent corresponding to the first topic of interest associated with thefirst node.
 2. The method of claim 1, wherein a hash function is used toderive a topic ID from a topic of interest.
 3. The method of claim 2,wherein a distributed hash table is used to derive the topic ID from thetopic of interest.
 4. The method of claim 1, wherein a global set oftopics of interest is updated each time a new topic of interest isgenerated by a node in the network, and wherein the global set of topicsof interest is distributed to the Total number of the nodes of thenetwork or a subset thereof in the network by way of a seconddistribution algorithm.
 5. The method of claim 4, wherein the seconddistribution algorithm is implemented based on a gossip algorithm. 6.The method of claim 1, wherein the gossip algorithm comprises a methodof distribution among neighboring nodes in a network, wherein each nodedistributes certain information to its neighbor nodes until the Totalnumber of the nodes of the network or a subset thereof in the networkhave received said certain information.
 7. The method of claim 5,wherein the gossip algorithm comprises a method of distribution amongneighboring nodes in a network, wherein each node distributes certaininformation to its neighbor nodes until the Total number of the nodes ofthe network or a subset thereof in the network have received saidcertain information.
 8. The method of claim 5, wherein a gossipalgorithm comprises a method of distribution of information among one ormore first nodes in a network, wherein each of the one or more firstnodes distributes certain information to a random selection of one ormore neighboring nodes in the network, and said one or more neighboringnodes which are randomly selected repeat the same distribution methoduntil the Total number of the nodes of the network or a subset thereofin the network have received said certain information.
 9. A data castingsystem in an interest aware peer network comprising a plurality ofnodes, the system comprising: a logic unit for determining whether a setof interests associated with a node in the network is updated, whereinthe set of interests comprises one or more topic IDs, wherein a topic IDcorresponds to a topic of interest associated with said node; wherein afirst topic ID identifies a first channel of communication over whichfirst content associated with the first topic ID is communicated to afirst node, and wherein a first set of interests identifies one or moresubscribers interested in receiving the first content via the firstnode, and wherein an updated set of interests associated with the firstnode identifies an updated group of subscribers interested in receivingthe first content; a logic unit for distributing the updated set ofinterests associated with the node to one or more neighboring nodes byway of a first distribution until the total number of the nodes of thenetwork or a subset thereof in the network are aware of the updated setof interests for the first node, wherein the first distribution schemeis implemented based on a gossip algorithm, wherein the gossip algorithmcomprises a method of distribution of information among one or morefirst nodes in a network, wherein each of the one or more first nodesdistributes information to a random selection of one or more nodes amonga plurality of neighboring nodes in the network, and said one or moreneighboring nodes which are randomly selected repeat the samedistribution method at least once until the total number of the nodes ofthe network or a subset thereof in the network have received saidinformation; wherein the one or more neighboring nodes determine valueof a first topic of interest in the first set of interests received inassociation with the first node, based on the topic ID included in thefirst set of interests for the first node; and wherein the first nodepublishes or subscribes to content corresponding to the first topic ofinterest associated with the first node.
 10. The system of claim 9,wherein a hash function is used to derive a topic ID from a topic ofinterest.
 11. The system of claim 10, wherein a distributed hash tableis used to derive the topic ID from the topic of interest.
 12. Thesystem of claim 9, wherein a global set of topics of interest is updatedeach time a new topic of interest is generated by a node in the network,and wherein the global set of topics of interest is distributed to theTotal number of the nodes of the network or a subset thereof in thenetwork by way of a second distribution algorithm.
 13. The system ofclaim 12, wherein the second distribution algorithm is implemented basedon a gossip algorithm.
 14. The system of claim 9, wherein the gossipalgorithm comprises a method of distribution among neighboring nodes ina network, wherein each node distributes certain information to itsneighbor nodes until the Total number of the nodes of the network or asubset thereof in the network have received said certain information.15. The system of claim 13, wherein the gossip algorithm comprises amethod of distribution among neighboring nodes in a network, whereineach node distributes certain information to its neighbor nodes untilthe Total number of the nodes of the network or a subset thereof in thenetwork have received said certain information.
 16. The system of claim13, wherein a gossip algorithm comprises a method of distribution ofinformation among one or more first nodes in a network, wherein each ofthe one or more first nodes distributes certain information to a randomselection of one or more neighboring nodes in the network, and said oneor more neighboring nodes which are randomly selected repeat the samedistribution method until the Total number of the nodes of the networkor a subset thereof in the network have received said certaininformation.
 17. A computer program product comprising a non-transitorydata storage medium having a computer readable program, wherein thecomputer readable program when executed on a computer causes thecomputer to: determine whether a set of interests associated with a nodein the network is updated, wherein the set of interests comprises one ormore topic IDs, wherein each topic ID corresponds to a topic of interestassociated with said node; wherein a first topic ID identifies a firstchannel of communication over which first content associated with thefirst topic ID is communicated to a first node, and wherein a first setof interests identifies one or more subscribers interested in receivingthe first content via the first node, and wherein an updated set ofinterests associated with the first node identifies an updated group ofsubscribers interested in receiving the first content; distribute theupdated set of interests associated with the node to one or moreneighboring nodes by way of a first distribution scheme until the totalnumber of the nodes of the network or a subset thereof in the networkare aware of the updated set of interests for the first node, whereinthe first distribution scheme is implemented based on a gossipalgorithm, wherein the gossip algorithm comprises a method ofdistribution of information among one or more first nodes in a network,wherein each of the one or more first nodes distributes information to arandom selection of one or more nodes among a plurality of neighboringnodes in the network, and said one or more neighboring nodes which arerandomly selected repeat the same distribution method at least onceuntil the total number of the nodes of the network or a subset thereofin the network have received said information; wherein the one or moreneighboring nodes determine value of a first topic of interest in thefirst set of interests received in association with the first node,based on the topic ID included in the first set of interests for thefirst node; and wherein the first node subscribes or publishes tocontent corresponding to the first topic of interest associated with thefirst node.
 18. The computer program product of claim 17, wherein a hashfunction is used to derive a topic ID from a topic of interest.
 19. Thecomputer program product of claim 18, wherein a distributed hash tableis used to derive the topic ID from the topic of interest.
 20. Thecomputer program product of claim 17, wherein a global set of topics ofinterest is updated each time a new topic of interest is generated by anode in the network, and wherein the global set of topics of interest isdistributed to the Total number of the nodes of the network or a subsetthereof in the network by way of a second distribution algorithm.